1. Field of the Invention
This invention is directed to methods and apparatus for aligning two sets of medical imaging data, in particular two sets, captured using different medical imaging modalities, showing an anatomical feature of a subject.
2. Description of the Prior Art
In the medical imaging field, several imaging schemes are known. For example PET (Positron Emission Tomography) is a method for imaging a subject in 3D using an injected radio-active substance which is processed in the body, typically resulting in an image indicating one or more biological functions. Other such functional imaging modalities are known, such as SPECT.
In such functional images, many important pathologies and anatomical structures appear as very high (or low) intensities. For example, a tumor in an FDG-PET image will often appear as a bright region
In cardiac medical imaging, CTA is a high resolution gated cardiac scan that enables the clinician to assess the coronary arteries, the wall motion of the heart and the thickness of the myocardium in order to assess the likelihood and extent of coronary artery disease.
PET (or SPECT) is used in numerous complementary ways, either gated, dynamic, or static, to assess the perfusion of the LV and effects that any CAD is having on the left ventricle (LV) that may not be picked up by the CTA. In particular CFR, which looks at the coronary flow reserve between rest and stress, often highlighting problems before they become apparent on the CTA.
In many cases CTA and PET (or SPECT) are used in combination with each other in order to more accurately assess the diagnosis and extent of cardiac disease. However in order to do this a good alignment between the CTA and PET is required. It is useful for example to assess the positions of the coronaries relative to the PET data in order to see if low flow/perfusion/CFR in the PET is due to a particular blockage in one of the coronaries.
Alignment becomes difficult because the CTA images are ‘snapshots’ of single timepoints, whereas the PET data are averaged over a period of time, and therefore the question arises: what is a good alignment?
The CT application ‘Circulation’ has tried to align such different types of data using a Mutual Information based registration directly on the two datasets. This approach runs into a series of problems as often the whole body may be well aligned but the LV itself is poorly aligned.
Examples of such alignments are given in FIG. 1. FIG. 1 illustrates a series of different types of alignments, each having significant difficulties in producing a good alignment between the two images. The CTA image (104) is detailed, whereas the data from the PET image (102) is more blurred, having been averaged over the time period of the PET scan. From the initial alignment, mutual information is used, then correlation ratio, then normalized mutual information; each has difficulty in one way or another.
Similar issues arise in other modalities matching averaged medical images with short or snapshot images.